Quantization-based filtering in distributed discrete event simulation

✍ Scribed by Bernard P Zeigler; Hyup J Cho; Jeong G Kim; Hessam S Sarjoughian; Jong.S Lee

• Publisher: Elsevier Science
• Year: 2002
• Tongue: English
• Weight: 597 KB
• Volume: 62
• Category: Article
• ISSN: 0743-7315
• DOI: 10.1016/s0743-7315(02)00002-3

✦ Synopsis

Quantization is an approach to distributed logical simulation in which the value space is quantized and trajectories are represented by the crossings of a set of thresholds. This is an alternative to the common approach which discretizes the time base of a continuous trajectory to obtain a finite number of equally spaced sampled values over time. In distributed simulation, a quantizer checks for threshold crossings whenever an output event occurs and sends this value across to a receiver thereby reducing the number of messages exchanged among federates in a federation. This may increase performance in various ways such as decreasing overall execution time or allowing a larger number of entities to be simulated. Predictive quantization is a more advanced approach that sends just one bit of information instead of the actual real value size with the consequence that not only the number of messages, but also the message size, can be significantly reduced in this approach. In this paper, we present an approach to packaging individual bits into a large message packet, called multiplexed predictive quantization. We demonstrate that this approach can save significant overhead (thereby maximizing data transmission) and can reach close to 100% efficiency in the limit of large numbers of simultaneous message sources encapsulated within individual federates. We also discuss the tradeoff between message bandwidth utilization and the error incurred in the quantization. The results relate bandwidth utilization and error to quantum size for federations executing in the HLA-compliant discrete event distributed simulation environment, DEVS/HLA. The theoretical and empirical results indicate that quantization can be very scaleable due to reduced local computation demands as well as having extremely favorable network load reduction/simulation fidelity tradeoffs.